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Christopher Hesse
Researcher at OpenAI
Publications - 12
Citations - 14287
Christopher Hesse is an academic researcher from OpenAI. The author has contributed to research in topics: Reinforcement learning & Benchmark (computing). The author has an hindex of 10, co-authored 12 publications receiving 4326 citations.
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Proceedings Article
Language Models are Few-Shot Learners
Tom B. Brown,Benjamin Mann,Nick Ryder,Melanie Subbiah,Jared Kaplan,Prafulla Dhariwal,Arvind Neelakantan,Pranav Shyam,Girish Sastry,Amanda Askell,Sandhini Agarwal,Ariel Herbert-Voss,Gretchen Krueger,Thomas Henighan,Rewon Child,Aditya Ramesh,Daniel M. Ziegler,Jeffrey Wu,Clemens Winter,Christopher Hesse,Mark Chen,Eric Sigler,Mateusz Litwin,Scott Gray,Benjamin Chess,Jack Clark,Christopher Berner,Samuel McCandlish,Alec Radford,Ilya Sutskever,Dario Amodei +30 more
TL;DR: GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic.
Posted Content
Language Models are Few-Shot Learners
Tom B. Brown,Benjamin Mann,Nick Ryder,Melanie Subbiah,Jared Kaplan,Prafulla Dhariwal,Arvind Neelakantan,Pranav Shyam,Girish Sastry,Amanda Askell,Sandhini Agarwal,Ariel Herbert-Voss,Gretchen Krueger,Thomas Henighan,Rewon Child,Aditya Ramesh,Daniel M. Ziegler,Jeffrey Wu,Clemens Winter,Christopher Hesse,Mark Chen,Eric Sigler,Mateusz Litwin,Scott Gray,Benjamin Chess,Jack Clark,Christopher Berner,Samuel McCandlish,Alec Radford,Ilya Sutskever,Dario Amodei +30 more
TL;DR: This article showed that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches.
Posted Content
Dota 2 with Large Scale Deep Reinforcement Learning
Christopher Berner,Greg Brockman,Brooke Chan,Vicki Cheung,Przemyslaw Debiak,Christy Dennison,David Farhi,Quirin Fischer,Shariq Hashme,Christopher Hesse,Rafal Jozefowicz,Scott Gray,Catherine Olsson,Jakub Pachocki,Michael Petrov,Henrique Ponde de Oliveira Pinto,Jonathan Raiman,Tim Salimans,Jeremy Schlatter,Jonas Schneider,Szymon Sidor,Ilya Sutskever,Jie Tang,Filip Wolski,Susan Zhang +24 more
TL;DR: By defeating the Dota 2 world champion (Team OG), OpenAI Five demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task.
Proceedings Article
Quantifying Generalization in Reinforcement Learning
TL;DR: It is shown that deeper convolutional architectures improve generalization, as do methods traditionally found in supervised learning, including L2 regularization, dropout, data augmentation and batch normalization.
Proceedings Article
Leveraging Procedural Generation to Benchmark Reinforcement Learning
TL;DR: This work empirically demonstrate that diverse environment distributions are essential to adequately train and evaluate RL agents, thereby motivating the extensive use of procedural content generation and uses this benchmark to investigate the effects of scaling model size.